Li Zicong, Zhang Henggui
Biological Physics Group, Department of Physics and Astronomy, The University of Manchester, Manchester, United Kingdom.
Peng Cheng Laboratory, Shenzhen, China.
Front Cardiovasc Med. 2021 Mar 19;8:616585. doi: 10.3389/fcvm.2021.616585. eCollection 2021.
Electrocardiograms (ECG) provide information about the electrical activity of the heart, which is useful for diagnosing abnormal cardiac functions such as arrhythmias. Recently, several algorithms based on advanced structures of neural networks have been proposed for auto-detecting cardiac arrhythmias, but their performance still needs to be further improved. This study aimed to develop an auto-detection algorithm, which extracts valid features from 12-lead ECG for classifying multiple types of cardiac states. The proposed algorithm consists of the following components: (i) a preprocessing component that utilizes the frame blocking method to split an ECG recording into frames with a uniform length for all considered ECG recordings; and (ii) a binary classifier based on ResNet, which is combined with the attention-based bidirectional long-short term memory model. The developed algorithm was trained and tested on ECG data of nine types of cardiac states, fulfilling a task of multi-label classification. It achieved an averaged F1-score and area under the curve at 0.908 and 0.974, respectively. The frame blocking and bidirectional long-short term memory model represented an improved algorithm compared with others in the literature for auto-detecting and classifying multi-types of cardiac abnormalities.
心电图（ECG）可提供有关心脏电活动的信息，这对于诊断心律失常等心脏功能异常很有用。最近，已经提出了几种基于神经网络先进结构的算法来自动检测心律失常，但其性能仍需进一步提高。本研究旨在开发一种自动检测算法，该算法从12导联心电图中提取有效特征，以对多种心脏状态进行分类。所提出的算法由以下部分组成：（i）一个预处理组件，它利用帧块方法将心电图记录分割成具有统一长度的帧，适用于所有考虑的心电图记录；（ii）一个基于ResNet的二元分类器，它与基于注意力的双向长短期记忆模型相结合。所开发的算法在九种心脏状态的心电图数据上进行了训练和测试，完成了多标签分类任务。它分别实现了平均F1分数和曲线下面积为0.908和0.974。与文献中其他用于自动检测和分类多种心脏异常的算法相比，帧块和双向长短期记忆模型代表了一种改进算法。
